






from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  _lowerCamelCase    =  42
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None




def       _SCREAMING_SNAKE_CASE (							)				->						Node | None:
       _UpperCAmelCase							=    Node(1 )
       _UpperCAmelCase							=    Node(2 )
       _UpperCAmelCase							=    Node(3 )
       _UpperCAmelCase							=    Node(4 )
       _UpperCAmelCase							=    Node(5 )
       return tree




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						list[int]:
       return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						list[int]:
       return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						list[int]:
       return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						int:
       return (max(height(root.left )   ,      height(root.right ) ) + 1) if root else 0




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Sequence[Node | None]:
       _UpperCAmelCase							=    []

       if root is None:
              return output

       _UpperCAmelCase							=    deque([root] )

       while process_queue:
              _UpperCAmelCase							=    process_queue.popleft()
              output.append(node.data )

              if node.left:
                     process_queue.append(node.left )
              if node.right:
                     process_queue.append(node.right )
       return output




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Sequence[Node | None]:
       _UpperCAmelCase							=    []

       def populate_output(__snake_case   ,      __snake_case ) -> None:
              if not root:
                     return
              if level == 1:
                     output.append(root.data )
              elif level > 1:
                     populate_output(root.left   ,      level - 1 )
                     populate_output(root.right   ,      level - 1 )

       populate_output(__snake_case   ,      __snake_case )
       return output




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Sequence[Node | None]:
       _UpperCAmelCase							=    []

       def populate_output(__snake_case   ,      __snake_case ) -> None:
              if root is None:
                     return
              if level == 1:
                     output.append(root.data )
              elif level > 1:
                     populate_output(root.right   ,      level - 1 )
                     populate_output(root.left   ,      level - 1 )

       populate_output(__snake_case   ,      __snake_case )
       return output




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Sequence[Node | None] | list[Any]:
       if root is None:
              return []

       _UpperCAmelCase							=    []

       _UpperCAmelCase							=    0
       _UpperCAmelCase							=    height(__snake_case )

       for h in range(1   ,      height_tree + 1 ):
              if not flag:
                     output.append(get_nodes_from_left_to_right(__snake_case   ,      __snake_case ) )
                     _UpperCAmelCase							=    1
              else:
                     output.append(get_nodes_from_right_to_left(__snake_case   ,      __snake_case ) )
                     _UpperCAmelCase							=    0

       return output




def       _SCREAMING_SNAKE_CASE (							)				->						None:  # Main function for testing.
       _UpperCAmelCase							=    make_tree()
       print(f"""In-order Traversal: {inorder(__snake_case )}""" )
       print(f"""Pre-order Traversal: {preorder(__snake_case )}""" )
       print(f"""Post-order Traversal: {postorder(__snake_case )}"""   ,      """\n""" )

       print(f"""Height of Tree: {height(__snake_case )}"""   ,      """\n""" )

       print("""Complete Level Order Traversal: """ )
       print(level_order(__snake_case )   ,      """\n""" )

       print("""Level-wise order Traversal: """ )

       for level in range(1   ,      height(__snake_case ) + 1 ):
              print(f"""Level {level}:"""   ,      get_nodes_from_left_to_right(__snake_case   ,      level=__snake_case ) )

       print("""\nZigZag order Traversal: """ )
       print(zigzag(__snake_case ) )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_						:  int				  =						logging.get_logger(__name__)


if is_vision_available():
				import PIL





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: int       =    ["""pixel_values"""]






							def __init__(       self       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = PILImageResampling.BICUBIC       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = True       ,   __snake_case = 1 / 255       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = True       ,   **__snake_case       ,   ):
											super().__init__(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			int							= size if size is not None else {"""shortest_edge""": 224}
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= get_size_dict(__snake_case       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											_SCREAMING_SNAKE_CASE					:			Any							= get_size_dict(__snake_case       ,   default_to_square=__snake_case       ,   param_name="""crop_size"""    )

											_SCREAMING_SNAKE_CASE					:			str							= do_resize
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= size
											_SCREAMING_SNAKE_CASE					:			List[str]							= resample
											_SCREAMING_SNAKE_CASE					:			str							= do_center_crop
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= crop_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= do_rescale
											_SCREAMING_SNAKE_CASE					:			Dict							= rescale_factor
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= do_normalize
											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= image_std if image_std is not None else OPENAI_CLIP_STD
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= do_convert_rgb






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = PILImageResampling.BICUBIC       ,   __snake_case = None       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			int							= get_size_dict(__snake_case       ,   default_to_square=__snake_case    )
											if "shortest_edge" not in size:
															raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
											_SCREAMING_SNAKE_CASE					:			Any							= get_resize_output_image_size(__snake_case       ,   size=size["""shortest_edge"""]       ,   default_to_square=__snake_case    )
											return resize(__snake_case       ,   size=__snake_case       ,   resample=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= get_size_dict(__snake_case    )
											if "height" not in size or "width" not in size:
															raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""    )
											return center_crop(__snake_case       ,   size=(size["""height"""], size["""width"""])       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											return rescale(__snake_case       ,   scale=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											return normalize(__snake_case       ,   mean=__snake_case       ,   std=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = ChannelDimension.FIRST       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= do_resize if do_resize is not None else self.do_resize
											_SCREAMING_SNAKE_CASE					:			Any							= size if size is not None else self.size
											_SCREAMING_SNAKE_CASE					:			List[str]							= get_size_dict(__snake_case       ,   param_name="""size"""       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Any							= resample if resample is not None else self.resample
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= do_center_crop if do_center_crop is not None else self.do_center_crop
											_SCREAMING_SNAKE_CASE					:			str							= crop_size if crop_size is not None else self.crop_size
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= get_size_dict(__snake_case       ,   param_name="""crop_size"""       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= do_rescale if do_rescale is not None else self.do_rescale
											_SCREAMING_SNAKE_CASE					:			int							= rescale_factor if rescale_factor is not None else self.rescale_factor
											_SCREAMING_SNAKE_CASE					:			Tuple							= do_normalize if do_normalize is not None else self.do_normalize
											_SCREAMING_SNAKE_CASE					:			List[str]							= image_mean if image_mean is not None else self.image_mean
											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_std if image_std is not None else self.image_std
											_SCREAMING_SNAKE_CASE					:			Any							= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= make_list_of_images(__snake_case    )

											if not valid_images(__snake_case    ):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

											if do_resize and size is None:
															raise ValueError("""Size must be specified if do_resize is True."""    )

											if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""    )

											if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

											if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("""Image mean and std must be specified if do_normalize is True."""    )

											# PIL RGBA images are converted to RGB
											if do_convert_rgb:
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= [convert_to_rgb(__snake_case    ) for image in images]

											# All transformations expect numpy arrays.
											_SCREAMING_SNAKE_CASE					:			str							= [to_numpy_array(__snake_case    ) for image in images]

											if do_resize:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= [self.resize(image=__snake_case       ,   size=__snake_case       ,   resample=__snake_case    ) for image in images]

											if do_center_crop:
															_SCREAMING_SNAKE_CASE					:			Tuple							= [self.center_crop(image=__snake_case       ,   size=__snake_case    ) for image in images]

											if do_rescale:
															_SCREAMING_SNAKE_CASE					:			List[Any]							= [self.rescale(image=__snake_case       ,   scale=__snake_case    ) for image in images]

											if do_normalize:
															_SCREAMING_SNAKE_CASE					:			Dict							= [self.normalize(image=__snake_case       ,   mean=__snake_case       ,   std=__snake_case    ) for image in images]

											_SCREAMING_SNAKE_CASE					:			str							= [to_channel_dimension_format(__snake_case       ,   __snake_case    ) for image in images]

											_SCREAMING_SNAKE_CASE					:			int							= {"""pixel_values""": images}
											return BatchFeature(data=__snake_case       ,   tensor_type=__snake_case    )




